Cleidocranial dysplasia and novel RUNX2 variants: dental, craniofacial, and osseous manifestations

Cleidocranial dysplasia (CCD) is a skeletal disorder affecting cranial sutures, teeth, and clavicles, and is associated with the RUNX2 mutations. Although numerous patients have been described, a direct genotype–phenotype correlation for RUNX2 has been difficult to establish. Further cases must be studied to understand the clinical and genetic spectra of CCD. Objectives: To characterize detailed phenotypes and identify variants causing CCD in five unrelated patients and their family members. Methodology: Clinical and radiographic examinations were performed. Genetic variants were identified by exome and Sanger sequencing, data were analyzed by bioinformatics tools. Results: Three cases were sporadic and two were familial. Exome sequencing successfully detected the heterozygous pathogenic RUNX2 variants in all affected individuals. Three were novel, comprising a frameshift c.739delA (p.(Ser247Valfs*)) in exon 6 (Patient-1), a nonsense c.901C>T (p.(Gln301*)) in exon 7 (Patient-2 and affected mother), and a nonsense c.1081C>T (p.(Gln361*)) in exon 8 (Patient-3). Two previously reported variants were missense: the c.673C>T (p.(Arg225Trp)) (Patient-4) and c.674G>A (p.(Arg225Gln)) (Patient-5) in exon 5 within the Runt homology domain. Patient-1, Patient-2, and Patient-4 with permanent dentition had thirty, nineteen, and twenty unerupted teeth, respectively; whereas Patient-3 and Patient-5, with deciduous dentition, had normally developed teeth. All patients exhibited typical CCD features, but the following uncommon/unreported phenotypes were observed: left fourth ray brachymetatarsia (Patient-1), normal clavicles (Patient-2 and affected mother), phalangeal malformations (Patient-3), and normal primary dentition (Patient-3, Patient-5). Conclusions: The study shows that exome sequencing is effective to detect mutation across ethnics. The two p.Arg225 variants confirm that the Runt homology domain is vital for RUNX2 function. Here, we report a new CCD feature, unilateral brachymetatarsia, and three novel truncating variants, expanding the phenotypic and genotypic spectra of RUNX2 , as well as show that the CCD patients can have normal deciduous teeth, but must be monitored for permanent teeth anomalies

Can knowledgeable experts assess costs and outcomes as if they were ignorant? : An experiment within precision medicine evaluation

Objectives The purpose of this study is to evaluate the validity of the standard approach in expert judgment for evaluating precision medicines, in which experts are required to estimate outcomes as if they did not have access to diagnostic information, whereas in fact, they do. Methods Fourteen clinicians participated in an expert judgment task to estimate the cost and medical outcomes of the use of exome sequencing in pediatric patients with intractable epilepsy in Thailand. Experts were randomly assigned to either an “unblind” or “blind” group; the former was provided with the exome sequencing results for each patient case prior to the judgment task, whereas the latter was not provided with the exome sequencing results. Both groups were asked to estimate the outcomes for the counterfactual scenario, in which patients had not been tested by exome sequencing. Results Our study did not show significant results, possibly due to the small sample size of both participants and case studies. Conclusions A comparison of the unblind and blind approach did not show conclusive evidence that there is a difference in outcomes. However, until further evidence suggests otherwise, we recommend the blind approach as preferable when using expert judgment to evaluate precision medicines because this approach is more representative of the counterfactual scenario than the unblind approach

AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes

IntroductionMandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes.MethodsThe training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set.ResultsWe trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838–0.999] (p < 0.001) and an AUC (Area Under the Curve) of 0.975 within controls (binary classification). Balanced accuracies were 0.811 [0.648–0.920] (p = 0.002) in a first multiclass design (MFDM vs. controls and differential diagnoses) and 0.813 [0.544–0.960] (p = 0.003) in a second multiclass design (MFDM vs. differential diagnoses).ConclusionThis is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers

AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes

International audienceIntroduction Mandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes. Methods The training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set. Results We trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838–0.999] ( p < 0.001) and an AUC (Area Under the Curve) of 0.975 within controls (binary classification). Balanced accuracies were 0.811 [0.648–0.920] ( p = 0.002) in a first multiclass design (MFDM vs. controls and differential diagnoses) and 0.813 [0.544–0.960] ( p = 0.003) in a second multiclass design (MFDM vs. differential diagnoses). Conclusion This is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers

Image1_AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes.jpeg

IntroductionMandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes.MethodsThe training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set.ResultsWe trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838–0.999] (p ConclusionThis is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers.</p

Image2_AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes.jpeg

IntroductionMandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes.MethodsThe training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set.ResultsWe trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838–0.999] (p ConclusionThis is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers.</p

Table1_AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes.docx

IntroductionMandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes.MethodsThe training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set.ResultsWe trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838–0.999] (p ConclusionThis is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers.</p